Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 68806

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 75451

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-26T21:37:38+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	142109
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	140226	0
dateAnnounced	0	1.00	10	10	0	303	0
gender	38036	0.73	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	137944	0.03	8	23	0	8	0
notes	76371	0.46	1	270	0	62794	1
mhlwPatientNumber	141660	0.00	1	11	0	434	0
prefecturePatientNumber	33986	0.76	5	20	0	108114	0
prefectureSourceURL	110717	0.22	5	224	0	3454	0
residence	46191	0.67	1	38	0	1429	0
sourceURL	1170	0.99	1	239	0	8810	0
relatedPatients	130243	0.08	2	259	0	7080	0
knownCluster	139581	0.02	3	88	0	234	0
detectedCityTown	114007	0.20	2	22	0	667	0
cityPrefectureNumber	114308	0.20	1	34	0	27792	2
citySourceURL	130032	0.08	9	317	0	3683	0
deceasedDate	140103	0.01	10	10	0	253	0
deceasedReportedDate	140889	0.01	10	62	0	207	0
deathSourceURL	141034	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 140225, FAL: 1884
charterFlightPassenger	142095	0	1.00	TRU: 14
cruisePassengerDisembarked	142098	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	27.55	24.76	-1	-1	20	40	100	▇▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	138745
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	138745	0
knownCluster	136267	0.02	3	88	0	231	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-26	2020-09-03	300
deceasedDate	138366	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	138416	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	36264	0.74	FALSE	2	M: 57404, F: 45077
patientStatus	136234	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 36349, 20: 27683, 30: 17764, 40: 14897
pcode	0	1.00	FALSE	47	13: 39128, 27: 18785, 14: 11913, 23: 9410
pref	0	1.00	FALSE	47	東京都: 39128, 大阪府: 18785, 神奈川: 11913, 愛知県: 9410
region	0	1.00	FALSE	8	関東地: 68806, 近畿地: 29592, 中部地: 14735, 九州地: 12647
広域圏	12148	0.91	FALSE	8	首都圏: 69142, 近畿圏: 28811, 中部圏: 13250, 九州圏: 8510
通俗的区分	0	1.00	FALSE	11	関東: 68806, 関西: 28811, 東海: 12597, 九州: 8510
fct_pref	0	1.00	FALSE	47	Tok: 39128, Osa: 18785, Kan: 11913, Aic: 9410

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 138745
charterFlightPassenger	138738	0	1.00	TRU: 7
cruisePassengerDisembarked	138734	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 136267, TRU: 2478

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.1）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.1）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 245.7176 258.4142 244.7754 246.7647 234.7679 228.4970 249.8857 250.5119
##  [9] 263.0201 261.5457 265.2094 264.0697 268.6249 270.9266
## 
## $北海道地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 232.9919 226.2553
## 46.42857 243.6203 235.7890
## 46.57143 226.0738 216.1737
## 46.71429 226.9969 216.5324
## 46.85714 213.5871 202.3746
## 47.00000 206.5426 194.9207
## 47.14286 226.5070 214.1311
## 47.28571 223.9288 209.8566
## 47.42857 233.7860 218.3104
## 47.57143 229.5063 212.5457
## 47.71429 231.1012 213.0454
## 47.85714 228.0104 208.9218
## 48.00000 230.7889 210.7597
## 48.14286 231.1389 210.0766
## 
## $北海道地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 258.4433 265.1799
## 46.42857 273.2080 281.0393
## 46.57143 263.4771 273.3771
## 46.71429 266.5326 276.9971
## 46.85714 255.9487 267.1612
## 47.00000 250.4514 262.0734
## 47.14286 273.2644 285.6403
## 47.28571 277.0950 291.1672
## 47.42857 292.2542 307.7298
## 47.57143 293.5850 310.5457
## 47.71429 299.3176 317.3734
## 47.85714 300.1290 319.2176
## 48.00000 306.4609 326.4901
## 48.14286 310.7142 331.7765
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 37.45446 32.66039 31.08279 31.20783 32.72199 33.86771 34.71404 34.72721
##  [9] 34.56100 34.15098 33.97013 33.83359 33.90562 33.95055
## 
## $東北地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 30.49046 26.80394
## 46.42857 25.31888 21.43252
## 46.57143 23.62300 19.67403
## 46.71429 23.74533 19.79492
## 46.85714 25.18875 21.20090
## 47.00000 26.23950 22.20137
## 47.14286 26.71833 22.48566
## 47.28571 26.42249 22.02624
## 47.42857 25.85928 21.25286
## 47.57143 25.19712 20.45724
## 47.71429 24.75368 19.87478
## 47.85714 24.43180 19.45480
## 48.00000 24.29070 19.20087
## 48.14286 24.15086 18.96321
## 
## $東北地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 44.41846 48.10498
## 46.42857 40.00189 43.88825
## 46.57143 38.54258 42.49155
## 46.71429 38.67033 42.62074
## 46.85714 40.25522 44.24307
## 47.00000 41.49592 45.53406
## 47.14286 42.70975 46.94242
## 47.28571 43.03192 47.42817
## 47.42857 43.26273 47.86915
## 47.57143 43.10483 47.84471
## 47.71429 43.18658 48.06548
## 47.85714 43.23537 48.21237
## 48.00000 43.52053 48.61036
## 48.14286 43.75024 48.93789
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 1083.4587 1091.2689  815.7616  600.9381  652.0004  828.8917  987.6128
##  [8] 1015.2471 1073.0966  857.2264  681.6830  739.1305  895.3019 1022.6799
## 
## $関東地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%      95%
## 46.28571 1008.4497 968.7424
## 46.42857  995.7673 945.2118
## 46.57143  712.5172 657.8629
## 46.71429  494.7004 438.4616
## 46.85714  543.7044 486.3760
## 47.00000  717.5649 658.6321
## 47.14286  871.0805 809.3919
## 47.28571  883.9756 814.4846
## 47.42857  929.2105 853.0417
## 47.57143  704.3961 623.4926
## 47.71429  522.9880 438.9799
## 47.85714  576.2914 490.0896
## 48.00000  728.8492 640.7346
## 48.14286  852.3687 762.2114
## 
## $関東地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 1158.4677 1198.1750
## 46.42857 1186.7706 1237.3261
## 46.57143  919.0061  973.6604
## 46.71429  707.1758  763.4146
## 46.85714  760.2963  817.6247
## 47.00000  940.2185  999.1513
## 47.14286 1104.1452 1165.8337
## 47.28571 1146.5186 1216.0096
## 47.42857 1216.9827 1293.1514
## 47.57143 1010.0567 1090.9602
## 47.71429  840.3780  924.3861
## 47.85714  901.9697  988.1715
## 48.00000 1061.7545 1149.8692
## 48.14286 1192.9911 1283.1484
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 339.5993 367.4597 310.7849 280.1850 247.2458 288.0277 286.4819 296.8574
##  [9] 321.8461 310.9514 314.8087 311.3244 335.8499 327.3164
## 
## $中部地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 316.5643 304.3704
## 46.42857 339.4096 324.5608
## 46.57143 278.5456 261.4792
## 46.71429 245.5494 227.2145
## 46.85714 211.0372 191.8696
## 47.00000 250.6101 230.8025
## 47.14286 247.7789 227.2908
## 47.28571 251.5034 227.4944
## 47.42857 271.3490 244.6175
## 47.57143 255.0123 225.3999
## 47.71429 254.2116 222.1334
## 47.85714 246.9085 212.8089
## 48.00000 268.4440 232.7615
## 48.14286 257.4935 220.5315
## 
## $中部地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 362.6343 374.8282
## 46.42857 395.5097 410.3585
## 46.57143 343.0241 360.0905
## 46.71429 314.8205 333.1554
## 46.85714 283.4543 302.6220
## 47.00000 325.4452 345.2529
## 47.14286 325.1849 345.6731
## 47.28571 342.2115 366.2205
## 47.42857 372.3431 399.0746
## 47.57143 366.8905 396.5028
## 47.71429 375.4058 407.4840
## 47.85714 375.7402 409.8399
## 48.00000 403.2558 438.9383
## 48.14286 397.1394 434.1014
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 614.8379 659.3563 663.4035 467.6637 505.3679 592.5091 665.0568 675.8537
##  [9] 717.8608 730.1379 563.8943 549.4233 633.9351 699.2939
## 
## $近畿地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 571.0992 547.9454
## 46.42857 606.8656 579.0787
## 46.57143 608.1319 578.8729
## 46.71429 409.7446 379.0840
## 46.85714 444.9170 412.9163
## 47.00000 529.6284 496.3413
## 47.14286 599.8366 565.3112
## 47.28571 600.1486 560.0728
## 47.42857 635.9291 592.5571
## 47.57143 644.2780 598.8265
## 47.71429 474.2781 426.8382
## 47.85714 456.2021 406.8538
## 48.00000 537.2432 486.0577
## 48.14286 599.2517 546.2926
## 
## $近畿地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 658.5766 681.7304
## 46.42857 711.8470 739.6339
## 46.57143 718.6752 747.9342
## 46.71429 525.5829 556.2435
## 46.85714 565.8188 597.8195
## 47.00000 655.3898 688.6769
## 47.14286 730.2769 764.8023
## 47.28571 751.5587 791.6345
## 47.42857 799.7925 843.1645
## 47.57143 815.9979 861.4494
## 47.71429 653.5105 700.9504
## 47.85714 642.6445 691.9928
## 48.00000 730.6269 781.8125
## 48.14286 799.3361 852.2953
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 30.91136 30.12616 30.12616 30.12616 30.12616 30.12616 30.12616 30.12616
##  [9] 30.12616 30.12616 30.12616 30.12616 30.12616 30.12616
## 
## $中国地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 21.41112 16.38200
## 46.42857 20.02233 14.67368
## 46.57143 19.76143 14.27467
## 46.71429 19.50694 13.88547
## 46.85714 19.25841 13.50537
## 47.00000 19.01544 13.13377
## 47.14286 18.77766 12.77013
## 47.28571 18.54477 12.41395
## 47.42857 18.31647 12.06480
## 47.57143 18.09250 11.72226
## 47.71429 17.87262 11.38599
## 47.85714 17.65662 11.05565
## 48.00000 17.44430 10.73093
## 48.14286 17.23547 10.41156
## 
## $中国地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 40.41161 45.44073
## 46.42857 40.22999 45.57864
## 46.57143 40.49089 45.97765
## 46.71429 40.74538 46.36686
## 46.85714 40.99391 46.74695
## 47.00000 41.23689 47.11855
## 47.14286 41.47466 47.48219
## 47.28571 41.70755 47.83837
## 47.42857 41.93585 48.18753
## 47.57143 42.15982 48.53006
## 47.71429 42.37970 48.86633
## 47.85714 42.59570 49.19668
## 48.00000 42.80802 49.52140
## 48.14286 43.01685 49.84077
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 19.64613 19.48173 19.48173 19.48173 19.48173 19.48173 19.48173 19.48173
##  [9] 19.48173 19.48173 19.48173 19.48173 19.48173 19.48173
## 
## $四国地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 15.85302 13.845070
## 46.42857 15.23180 12.982029
## 46.57143 15.00527 12.635583
## 46.71429 14.78967 12.305843
## 46.85714 14.58355 11.990604
## 47.00000 14.38575 11.688105
## 47.14286 14.19535 11.396916
## 47.28571 14.01158 11.115857
## 47.42857 13.83378 10.843938
## 47.57143 13.66141 10.580322
## 47.71429 13.49400 10.324291
## 47.85714 13.33115 10.075226
## 48.00000 13.17250  9.832589
## 48.14286 13.01774  9.595904
## 
## $四国地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 23.43924 25.44719
## 46.42857 23.73165 25.98143
## 46.57143 23.95818 26.32787
## 46.71429 24.17379 26.65761
## 46.85714 24.37991 26.97285
## 47.00000 24.57770 27.27535
## 47.14286 24.76810 27.56654
## 47.28571 24.95188 27.84760
## 47.42857 25.12968 28.11952
## 47.57143 25.30205 28.38313
## 47.71429 25.46945 28.63917
## 47.85714 25.63231 28.88823
## 48.00000 25.79096 29.13087
## 48.14286 25.94572 29.36755
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 148.29522 118.30829  95.84738  85.12781  99.49757 119.57628 146.07698
##  [8] 143.73360 134.67893 119.86491 109.62897 116.48144 128.92963 149.35398
## 
## $九州地方$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 125.79627 113.88604
## 46.42857  90.72917  76.12967
## 46.57143  65.08117  48.79453
## 46.71429  53.57289  36.86872
## 46.85714  66.35490  48.81022
## 47.00000  82.73322  63.22968
## 47.14286 106.19578  85.08395
## 47.28571  98.04554  73.85973
## 47.42857  84.24856  57.55234
## 47.57143  65.22482  36.30011
## 47.71429  52.38181  22.07700
## 47.85714  56.15081  24.21370
## 48.00000  64.99443  31.14917
## 48.14286  82.20676  46.66117
## 
## $九州地方$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 170.7942 182.7044
## 46.42857 145.8874 160.4869
## 46.57143 126.6136 142.9002
## 46.71429 116.6827 133.3869
## 46.85714 132.6402 150.1849
## 47.00000 156.4193 175.9229
## 47.14286 185.9582 207.0700
## 47.28571 189.4217 213.6075
## 47.42857 185.1093 211.8055
## 47.57143 174.5050 203.4297
## 47.71429 166.8761 197.1809
## 47.85714 176.8121 208.7492
## 48.00000 192.8648 226.7101
## 48.14286 216.5012 252.0468

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 245.7176 258.4142 244.7754 246.7647 234.7679 228.4970 249.8857 250.5119
##  [9] 263.0201 261.5457 265.2094 264.0697 268.6249 270.9266
## 
## $北海道$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 232.9919 226.2553
## 46.42857 243.6203 235.7890
## 46.57143 226.0738 216.1737
## 46.71429 226.9969 216.5324
## 46.85714 213.5871 202.3746
## 47.00000 206.5426 194.9207
## 47.14286 226.5070 214.1311
## 47.28571 223.9288 209.8566
## 47.42857 233.7860 218.3104
## 47.57143 229.5063 212.5457
## 47.71429 231.1012 213.0454
## 47.85714 228.0104 208.9218
## 48.00000 230.7889 210.7597
## 48.14286 231.1389 210.0766
## 
## $北海道$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 258.4433 265.1799
## 46.42857 273.2080 281.0393
## 46.57143 263.4771 273.3771
## 46.71429 266.5326 276.9971
## 46.85714 255.9487 267.1612
## 47.00000 250.4514 262.0734
## 47.14286 273.2644 285.6403
## 47.28571 277.0950 291.1672
## 47.42857 292.2542 307.7298
## 47.57143 293.5850 310.5457
## 47.71429 299.3176 317.3734
## 47.85714 300.1290 319.2176
## 48.00000 306.4609 326.4901
## 48.14286 310.7142 331.7765
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 0.3839508 1.3123950 0.7747390 1.0860921 0.9057895 1.0102016 0.9497372
##  [8] 0.9847517 0.9644751 0.9762172 0.9694174 0.9733551 0.9710748 0.9723953
## 
## $青森県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 -2.139322 -3.475062
## 46.42857 -1.641342 -3.204956
## 46.57143 -2.258534 -3.864252
## 46.71429 -2.168485 -3.891354
## 46.85714 -2.469904 -4.256889
## 47.00000 -2.528095 -4.401156
## 47.14286 -2.717851 -4.659355
## 47.28571 -2.822108 -4.837338
## 47.42857 -2.968605 -5.050651
## 47.57143 -3.083695 -5.232883
## 47.71429 -3.210923 -5.423861
## 47.85714 -3.325479 -5.601145
## 48.00000 -3.442261 -5.778540
## 48.14286 -3.553004 -5.948606
## 
## $青森県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 2.907223 4.242963
## 46.42857 4.266132 5.829746
## 46.57143 3.808012 5.413730
## 46.71429 4.340669 6.063538
## 46.85714 4.281483 6.068468
## 47.00000 4.548498 6.421560
## 47.14286 4.617326 6.558830
## 47.28571 4.791612 6.806841
## 47.42857 4.897555 6.979601
## 47.57143 5.036130 7.185317
## 47.71429 5.149758 7.362696
## 47.85714 5.272189 7.547855
## 48.00000 5.384410 7.720689
## 48.14286 5.497795 7.893396
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 12.55133 18.33298 13.60175 11.91015 11.02215 11.34860 13.27884 14.69246
##  [9] 16.04874 15.40829 13.67689 11.66720 11.48654 12.70549
## 
## $岩手県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 11.139099 10.391511
## 46.42857 16.762676 15.931405
## 46.57143 11.860023 10.938006
## 46.71429 10.107892  9.153836
## 46.85714  9.002944  7.934043
## 47.00000  9.284007  8.191079
## 47.14286 11.166023 10.047564
## 47.28571 12.374225 11.147025
## 47.42857 13.466100 12.098934
## 47.57143 12.600543 11.114211
## 47.71429 10.712486  9.143226
## 47.85714  8.576519  6.940414
## 48.00000  8.313927  6.634449
## 48.14286  9.465524  7.750389
## 
## $岩手県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 13.96356 14.71114
## 46.42857 19.90329 20.73456
## 46.57143 15.34349 16.26550
## 46.71429 13.71240 14.66646
## 46.85714 13.04135 14.11025
## 47.00000 13.41319 14.50612
## 47.14286 15.39166 16.51012
## 47.28571 17.01070 18.23789
## 47.42857 18.63137 19.99854
## 47.57143 18.21604 19.70237
## 47.71429 16.64129 18.21055
## 47.85714 14.75787 16.39398
## 48.00000 14.65915 16.33862
## 48.14286 15.94546 17.66060
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 17.54127 16.28142 17.00114 16.73235 16.67098 16.93808 16.57471 16.94988
##  [9] 16.61938 16.87480 16.70373 16.79619 16.76765 16.75073
## 
## $宮城県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 12.41502 9.701352
## 46.42857 11.04603 8.274590
## 46.57143 11.54874 8.662412
## 46.71429 11.24892 8.346167
## 46.85714 10.92891 7.889238
## 47.00000 11.16247 8.105050
## 47.14286 10.60048 7.437914
## 47.28571 10.91375 7.718410
## 47.42857 10.44931 7.183071
## 47.57143 10.61058 7.294500
## 47.71429 10.34218 6.974574
## 47.85714 10.32317 6.896566
## 48.00000 10.21055 6.739425
## 48.14286 10.08194 6.551699
## 
## $宮城県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 22.66751 25.38118
## 46.42857 21.51680 24.28824
## 46.57143 22.45355 25.33987
## 46.71429 22.21579 25.11854
## 46.85714 22.41305 25.45272
## 47.00000 22.71369 25.77111
## 47.14286 22.54894 25.71151
## 47.28571 22.98602 26.18136
## 47.42857 22.78944 26.05568
## 47.57143 23.13902 26.45510
## 47.71429 23.06529 26.43289
## 47.85714 23.26920 26.69581
## 48.00000 23.32475 26.79587
## 48.14286 23.41951 26.94975
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 0.7150848 0.7634581 0.7634581 0.7634581 0.7634581 0.7634581 0.7634581
##  [8] 0.7634581 0.7634581 0.7634581 0.7634581 0.7634581 0.7634581 0.7634581
## 
## $秋田県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                 80%       95%
## 46.28571 -0.6202384 -1.327116
## 46.42857 -0.5859142 -1.300229
## 46.57143 -0.5875659 -1.302755
## 46.71429 -0.5892155 -1.305278
## 46.85714 -0.5908631 -1.307797
## 47.00000 -0.5925087 -1.310314
## 47.14286 -0.5941523 -1.312828
## 47.28571 -0.5957940 -1.315339
## 47.42857 -0.5974336 -1.317846
## 47.57143 -0.5990713 -1.320351
## 47.71429 -0.6007070 -1.322852
## 47.85714 -0.6023407 -1.325351
## 48.00000 -0.6039725 -1.327847
## 48.14286 -0.6056024 -1.330339
## 
## $秋田県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 2.050408 2.757285
## 46.42857 2.112831 2.827145
## 46.57143 2.114482 2.829671
## 46.71429 2.116132 2.832194
## 46.85714 2.117779 2.834714
## 47.00000 2.119425 2.837230
## 47.14286 2.121069 2.839744
## 47.28571 2.122710 2.842255
## 47.42857 2.124350 2.844762
## 47.57143 2.125988 2.847267
## 47.71429 2.127623 2.849769
## 47.85714 2.129257 2.852267
## 48.00000 2.130889 2.854763
## 48.14286 2.132519 2.857256
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 0.59722148 0.69197089 0.35009432 0.49523708 0.42178087 0.18855603
##  [7] 0.07949441 0.12244806 0.09273460 0.07178669 0.05881642 0.05298956
## [13] 0.05344788 0.05932888
## 
## $山形県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                 80%        95%
## 46.28571 -0.4111873 -0.9450066
## 46.42857 -0.3340243 -0.8771533
## 46.57143 -0.7007769 -1.2570746
## 46.71429 -0.5863416 -1.1588948
## 46.85714 -0.6943807 -1.2852409
## 47.00000 -0.9640365 -1.5741821
## 47.14286 -1.1095525 -1.7389959
## 47.28571 -1.0782924 -1.7139260
## 47.42857 -1.1359376 -1.7863573
## 47.57143 -1.1809510 -1.8441102
## 47.71429 -1.2140970 -1.8879366
## 47.85714 -1.2363725 -1.9189195
## 48.00000 -1.2489323 -1.9383707
## 48.14286 -1.2530235 -1.9477409
## 
## $山形県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 1.605630 2.139450
## 46.42857 1.717966 2.261095
## 46.57143 1.400966 1.957263
## 46.71429 1.576816 2.149369
## 46.85714 1.537942 2.128803
## 47.00000 1.341149 1.951294
## 47.14286 1.268541 1.897985
## 47.28571 1.323189 1.958822
## 47.42857 1.321407 1.971827
## 47.57143 1.324524 1.987684
## 47.71429 1.331730 2.005569
## 47.85714 1.342352 2.024899
## 48.00000 1.355828 2.045266
## 48.14286 1.371681 2.066399
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 3.78305 3.78305 3.78305 3.78305 3.78305 3.78305 3.78305 3.78305 3.78305
## [10] 3.78305 3.78305 3.78305 3.78305 3.78305
## 
## $福島県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%        95%
## 46.28571 1.1329956 -0.2698584
## 46.42857 1.0666604 -0.3713094
## 46.57143 1.0019069 -0.4703412
## 46.71429 0.9386272 -0.5671192
## 46.85714 0.8767250 -0.6617905
## 47.00000 0.8161140 -0.7544870
## 47.14286 0.7567167 -0.8453273
## 47.28571 0.6984629 -0.9344188
## 47.42857 0.6412891 -1.0218586
## 47.57143 0.5851373 -1.1077354
## 47.71429 0.5299546 -1.1921301
## 47.85714 0.4756924 -1.2751168
## 48.00000 0.4223063 -1.3567639
## 48.14286 0.3697550 -1.4371341
## 
## $福島県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 6.433104 7.835958
## 46.42857 6.499439 7.937409
## 46.57143 6.564193 8.036441
## 46.71429 6.627472 8.133219
## 46.85714 6.689375 8.227890
## 47.00000 6.749986 8.320587
## 47.14286 6.809383 8.411427
## 47.28571 6.867637 8.500518
## 47.42857 6.924811 8.587958
## 47.57143 6.980962 8.673835
## 47.71429 7.036145 8.758230
## 47.85714 7.090407 8.841216
## 48.00000 7.143793 8.922864
## 48.14286 7.196345 9.003234
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 46.09342 39.35835 37.60826 38.91973 38.91973 38.91973 38.91973 38.91973
##  [9] 38.91973 38.91973 38.91973 38.91973 38.91973 38.91973
## 
## $茨城県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 40.77450 37.95884
## 46.42857 33.59050 30.53718
## 46.57143 31.82414 28.76221
## 46.71429 33.06580 29.96692
## 46.85714 32.56579 29.20222
## 47.00000 32.10236 28.49346
## 47.14286 31.66848 27.82991
## 47.28571 31.25914 27.20387
## 47.42857 30.87059 26.60963
## 47.57143 30.49995 26.04279
## 47.71429 30.14495 25.49987
## 47.85714 29.80377 24.97807
## 48.00000 29.47490 24.47511
## 48.14286 29.15711 23.98909
## 
## $茨城県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 51.41234 54.22800
## 46.42857 45.12620 48.17952
## 46.57143 43.39238 46.45431
## 46.71429 44.77365 47.87253
## 46.85714 45.27366 48.63723
## 47.00000 45.73709 49.34599
## 47.14286 46.17097 50.00955
## 47.28571 46.58031 50.63558
## 47.42857 46.96886 51.22982
## 47.57143 47.33950 51.79666
## 47.71429 47.69450 52.33959
## 47.85714 48.03569 52.86138
## 48.00000 48.36455 53.36434
## 48.14286 48.68235 53.85037
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 7.742254 7.685010 8.724660 6.578986 8.146927 8.195592 6.689960 7.526416
##  [9] 7.526416 7.526416 7.526416 7.526416 7.526416 7.526416
## 
## $栃木県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 4.538851 2.843072
## 46.42857 4.417171 2.687282
## 46.57143 5.393632 3.630293
## 46.71429 3.185946 1.389779
## 46.85714 4.692988 2.864583
## 47.00000 4.681808 2.821724
## 47.14286 3.117334 1.226101
## 47.28571 3.760926 1.767597
## 47.42857 3.685907 1.652865
## 47.57143 3.612326 1.540331
## 47.71429 3.540102 1.429875
## 47.85714 3.469164 1.321384
## 48.00000 3.399445 1.214758
## 48.14286 3.330884 1.109904
## 
## $栃木県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%      95%
## 46.28571 10.945657 12.64144
## 46.42857 10.952848 12.68274
## 46.57143 12.055687 13.81903
## 46.71429  9.972026 11.76819
## 46.85714 11.600867 13.42927
## 47.00000 11.709376 13.56946
## 47.14286 10.262586 12.15382
## 47.28571 11.291905 13.28523
## 47.42857 11.366925 13.39997
## 47.57143 11.440506 13.51250
## 47.71429 11.512730 13.62296
## 47.85714 11.583668 13.73145
## 48.00000 11.653387 13.83807
## 48.14286 11.721948 13.94293
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 23.05292 20.82667 18.22494 21.72893 18.95958 20.64425 19.84148 20.09692
##  [9] 20.10850 20.00741 20.10941 20.03763 20.07740 20.06067
## 
## $群馬県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 18.31533 15.807398
## 46.42857 15.19462 12.213193
## 46.57143 12.27038  9.118232
## 46.71429 15.65817 12.444505
## 46.85714 12.44728  8.999874
## 47.00000 13.98606 10.461429
## 47.14286 12.89070  9.211185
## 47.28571 12.94259  9.155313
## 47.42857 12.73170  8.826650
## 47.57143 12.41555  8.396660
## 47.71429 12.31572  8.189996
## 47.85714 12.03944  7.805450
## 48.00000 11.88536  7.548755
## 48.14286 11.67617  7.237679
## 
## $群馬県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 27.79052 30.29845
## 46.42857 26.45872 29.44015
## 46.57143 24.17950 27.33165
## 46.71429 27.79969 31.01336
## 46.85714 25.47188 28.91928
## 47.00000 27.30244 30.82707
## 47.14286 26.79225 30.47177
## 47.28571 27.25124 31.03852
## 47.42857 27.48531 31.39036
## 47.57143 27.59927 31.61816
## 47.71429 27.90309 32.02882
## 47.85714 28.03581 32.26980
## 48.00000 28.26945 32.60605
## 48.14286 28.44518 32.88367
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 126.85502 140.67558  99.97747  94.76480  91.34349 118.01920 139.13941
##  [8] 118.72677 123.88005 109.95087 110.08561 108.12927 117.91906 126.16018
## 
## $埼玉県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 111.13469 102.81286
## 46.42857 124.01775 115.19964
## 46.57143  83.11373  74.18660
## 46.71429  77.75410  68.74919
## 46.85714  74.04393  64.88610
## 47.00000  99.60251  89.85330
## 47.14286 119.82777 109.60481
## 47.28571  96.31159  84.44572
## 47.42857 100.60418  88.28268
## 47.57143  86.11575  73.49820
## 47.71429  85.57631  72.60186
## 47.85714  82.81088  69.40813
## 48.00000  91.51662  77.54001
## 48.14286  98.85127  84.39481
## 
## $埼玉県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 142.5753 150.8972
## 46.42857 157.3334 166.1515
## 46.57143 116.8412 125.7683
## 46.71429 111.7755 120.7804
## 46.85714 108.6431 117.8009
## 47.00000 136.4359 146.1851
## 47.14286 158.4510 168.6740
## 47.28571 141.1419 153.0078
## 47.42857 147.1559 159.4774
## 47.57143 133.7860 146.4035
## 47.71429 134.5949 147.5694
## 47.85714 133.4477 146.8504
## 48.00000 144.3215 158.2981
## 48.14286 153.4691 167.9255
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 76.58500 82.97635 74.19841 72.42353 67.92305 72.01324 78.22371 75.94592
##  [9] 77.83011 74.08710 69.98720 66.79697 72.19379 72.36900
## 
## $千葉県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 64.25484 57.72765
## 46.42857 69.19182 61.89473
## 46.57143 59.86845 52.28264
## 46.71429 57.56816 49.70420
## 46.85714 52.56023 44.42764
## 47.00000 56.15920 47.76657
## 47.14286 61.89321 53.24836
## 47.28571 58.31599 48.98327
## 47.42857 59.42679 49.68466
## 47.57143 55.07264 45.00699
## 47.71429 50.38064 40.00155
## 47.85714 46.61567 35.93234
## 48.00000 51.45367 40.47452
## 48.14286 51.08473 39.81752
## 
## $千葉県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 88.91515  95.44235
## 46.42857 96.76088 104.05796
## 46.57143 88.52836  96.11418
## 46.71429 87.27890  95.14285
## 46.85714 83.28588  91.41847
## 47.00000 87.86729  96.25991
## 47.14286 94.55420 103.19905
## 47.28571 93.57586 102.90858
## 47.42857 96.23342 105.97555
## 47.57143 93.10156 103.16720
## 47.71429 89.59376  99.97285
## 47.85714 86.97827  97.66160
## 48.00000 92.93390 103.91306
## 48.14286 93.65326 104.92047
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 447.1234 478.4125 328.7010 261.0838 233.2561 338.6954 421.0525 396.0580
##  [9] 402.5802 281.0501 221.5443 234.2737 329.4787 406.0911
## 
## $東京都$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 397.3312 370.97272
## 46.42857 417.6377 385.46540
## 46.57143 258.8538 221.87902
## 46.71429 185.5768 145.60580
## 46.85714 155.4839 114.31373
## 47.00000 258.0382 215.34089
## 47.14286 338.8123 295.27700
## 47.28571 304.8963 256.63816
## 47.42857 305.0872 253.47755
## 47.57143 179.3672 125.53949
## 47.71429 117.4853  62.39973
## 47.85714 128.2283  72.09122
## 48.00000 222.1583 165.34626
## 48.14286 298.0627 240.87589
## 
## $東京都$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 496.9157 523.2741
## 46.42857 539.1874 571.3597
## 46.57143 398.5481 435.5229
## 46.71429 336.5908 376.5618
## 46.85714 311.0284 352.1985
## 47.00000 419.3526 462.0499
## 47.14286 503.2927 546.8280
## 47.28571 487.2198 535.4779
## 47.42857 500.0731 551.6828
## 47.57143 382.7331 436.5608
## 47.71429 325.6034 380.6889
## 47.85714 340.3191 396.4561
## 48.00000 436.7991 493.6112
## 48.14286 514.1195 571.3062
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 248.9168 216.7260 166.7538 108.2513  99.8879 160.3369 221.1555 232.7743
##  [9] 213.2745 178.2490 143.2792 136.1543 163.7982 199.7803
## 
## $神奈川県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 226.93079 215.29208
## 46.42857 191.06060 177.47415
## 46.57143 140.24413 126.21077
## 46.71429  81.73927  67.70463
## 46.85714  73.36356  59.32243
## 47.00000 133.79773 119.74874
## 47.14286 193.79307 179.30830
## 47.28571 200.52374 183.45131
## 47.42857 177.96921 159.27974
## 47.57143 141.80108 122.50673
## 47.71429 106.56409  87.12825
## 47.85714  99.31191  79.80870
## 48.00000 126.74129 107.12456
## 48.14286 161.89884 141.84562
## 
## $神奈川県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 270.9029 282.5416
## 46.42857 242.3914 255.9779
## 46.57143 193.2634 207.2968
## 46.71429 134.7634 148.7980
## 46.85714 126.4122 140.4534
## 47.00000 186.8761 200.9251
## 47.14286 248.5178 263.0026
## 47.28571 265.0249 282.0973
## 47.42857 248.5797 267.2692
## 47.57143 214.6969 233.9913
## 47.71429 179.9944 199.4302
## 47.85714 172.9968 192.5000
## 48.00000 200.8551 220.4718
## 48.14286 237.6617 257.7149
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 6.673394 6.673394 6.673394 6.673394 6.673394 6.673394 6.673394 6.673394
##  [9] 6.673394 6.673394 6.673394 6.673394 6.673394 6.673394
## 
## $新潟県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 3.631513 2.021239
## 46.42857 3.590410 1.958376
## 46.57143 3.549847 1.896340
## 46.71429 3.509804 1.835100
## 46.85714 3.470261 1.774625
## 47.00000 3.431201 1.714888
## 47.14286 3.392606 1.655862
## 47.28571 3.354460 1.597522
## 47.42857 3.316747 1.539845
## 47.57143 3.279453 1.482809
## 47.71429 3.242565 1.426393
## 47.85714 3.206069 1.370578
## 48.00000 3.169953 1.315343
## 48.14286 3.134206 1.260672
## 
## $新潟県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%      95%
## 46.28571  9.715275 11.32555
## 46.42857  9.756379 11.38841
## 46.57143  9.796942 11.45045
## 46.71429  9.836985 11.51169
## 46.85714  9.876527 11.57216
## 47.00000  9.915587 11.63190
## 47.14286  9.954182 11.69093
## 47.28571  9.992329 11.74927
## 47.42857 10.030042 11.80694
## 47.57143 10.067335 11.86398
## 47.71429 10.104224 11.92040
## 47.85714 10.140720 11.97621
## 48.00000 10.176836 12.03145
## 48.14286 10.212583 12.08612
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 2.353529 2.307005 2.262793 2.220778 2.180852 2.142910 2.106854 2.072590
##  [9] 2.040029 2.009087 1.979682 1.951739 1.925185 1.899950
## 
## $富山県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                  80%       95%
## 46.28571 -0.09029824 -1.383982
## 46.42857 -0.29094653 -1.666219
## 46.57143 -0.46687486 -1.911874
## 46.71429 -0.62259953 -2.127793
## 46.85714 -0.76143970 -2.318995
## 47.00000 -0.88593272 -2.489305
## 47.14286 -0.99807760 -2.641729
## 47.28571 -1.09948677 -2.778683
## 47.42857 -1.19148558 -2.902146
## 47.57143 -1.27518020 -3.013766
## 47.71429 -1.35150561 -3.114930
## 47.85714 -1.42126046 -3.206819
## 48.00000 -1.48513297 -3.290446
## 48.14286 -1.54372073 -3.366690
## 
## $富山県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 4.797356 6.091040
## 46.42857 4.904956 6.280228
## 46.57143 4.992460 6.437459
## 46.71429 5.064156 6.569350
## 46.85714 5.123144 6.680699
## 47.00000 5.171753 6.775126
## 47.14286 5.211786 6.855438
## 47.28571 5.244668 6.923864
## 47.42857 5.271545 6.982205
## 47.57143 5.293354 7.031940
## 47.71429 5.310870 7.074295
## 47.85714 5.324739 7.110297
## 48.00000 5.335503 7.140816
## 48.14286 5.343622 7.166591
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 2.544417 2.544417 2.544417 2.544417 2.544417 2.544417 2.544417 2.544417
##  [9] 2.544417 2.544417 2.544417 2.544417 2.544417 2.544417
## 
## $石川県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 -1.084982 -3.006270
## 46.42857 -1.289222 -3.318628
## 46.57143 -1.483118 -3.615166
## 46.71429 -1.668099 -3.898070
## 46.85714 -1.845291 -4.169062
## 47.00000 -2.015604 -4.429533
## 47.14286 -2.179780 -4.680619
## 47.28571 -2.338440 -4.923268
## 47.42857 -2.492103 -5.158276
## 47.57143 -2.641216 -5.386324
## 47.71429 -2.786159 -5.607995
## 47.85714 -2.927263 -5.823796
## 48.00000 -3.064820 -6.034170
## 48.14286 -3.199082 -6.239507
## 
## $石川県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 6.173816  8.095104
## 46.42857 6.378056  8.407462
## 46.57143 6.571952  8.704000
## 46.71429 6.756932  8.986904
## 46.85714 6.934125  9.257896
## 47.00000 7.104437  9.518366
## 47.14286 7.268614  9.769453
## 47.28571 7.427273 10.012101
## 47.42857 7.580937 10.247110
## 47.57143 7.730050 10.475158
## 47.71429 7.874992 10.696829
## 47.85714 8.016097 10.912630
## 48.00000 8.153653 11.123004
## 48.14286 8.287916 11.328341
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 2.109616 2.493218 2.264462 2.070601 1.906314 1.767088 1.649100 1.549111
##  [9] 1.464375 1.392566 1.331710 1.280138 1.236433 1.199395
## 
## $福井県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                  80%        95%
## 46.28571  0.09606688 -0.9698416
## 46.42857  0.34196276 -0.7968431
## 46.57143 -0.11147401 -1.3692184
## 46.71429 -0.45438957 -1.7910390
## 46.85714 -0.72051197 -2.1110695
## 47.00000 -0.93050261 -2.3585206
## 47.14286 -1.09818729 -2.5525134
## 47.28571 -1.23331993 -2.7062500
## 47.42857 -1.34302385 -2.8291712
## 47.57143 -1.43262887 -2.9281964
## 47.71429 -1.50619557 -3.0084921
## 47.85714 -1.56686161 -3.0739722
## 48.00000 -1.61707973 -3.1276382
## 48.14286 -1.65878607 -3.1718159
## 
## $福井県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 4.123165 5.189074
## 46.42857 4.644474 5.783280
## 46.57143 4.640397 5.898142
## 46.71429 4.595592 5.932242
## 46.85714 4.533139 5.923697
## 47.00000 4.464678 5.892696
## 47.14286 4.396387 5.850714
## 47.28571 4.331542 5.804472
## 47.42857 4.271774 5.757922
## 47.57143 4.217760 5.713327
## 47.71429 4.169616 5.671912
## 47.85714 4.127138 5.634248
## 48.00000 4.089946 5.600504
## 48.14286 4.057577 5.570606
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 2.727190 3.305741 4.204719 4.002967 4.040114 4.338799 4.665277 4.412375
##  [9] 4.331863 4.306232 4.298072 4.295475 4.294648 4.294384
## 
## $山梨県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%        95%
## 46.28571 0.8371087 -0.1634401
## 46.42857 1.2123649  0.1041984
## 46.57143 2.0312275  0.8806507
## 46.71429 1.7761696  0.5973743
## 46.85714 1.7678320  0.5649587
## 47.00000 2.0238898  0.7984511
## 47.14286 2.3091173  1.0618416
## 47.28571 2.0411718  0.7859326
## 47.42857 1.9340752  0.6647629
## 47.57143 1.8778077  0.5922776
## 47.71429 1.8379603  0.5356558
## 47.85714 1.8036208  0.4845131
## 48.00000 1.7713067  0.4355308
## 48.14286 1.7398990  0.3876362
## 
## $山梨県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 4.617272 5.617821
## 46.42857 5.399118 6.507284
## 46.57143 6.378210 7.528787
## 46.71429 6.229764 7.408560
## 46.85714 6.312395 7.515269
## 47.00000 6.653708 7.879146
## 47.14286 7.021437 8.268713
## 47.28571 6.783578 8.038818
## 47.42857 6.729651 7.998963
## 47.57143 6.734656 8.020186
## 47.71429 6.758184 8.060489
## 47.85714 6.787328 8.106436
## 48.00000 6.817988 8.153764
## 48.14286 6.848870 8.201132
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 13.68177 13.68177 13.68177 13.68177 13.68177 13.68177 13.68177 13.68177
##  [9] 13.68177 13.68177 13.68177 13.68177 13.68177 13.68177
## 
## $長野県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%      95%
## 46.28571 10.286014 8.488408
## 46.42857 10.019321 8.080536
## 46.57143  9.770771 7.700412
## 46.71429  9.537100 7.343043
## 46.85714  9.315918 7.004774
## 47.00000  9.105414 6.682835
## 47.14286  8.904175 6.375067
## 47.28571  8.711077 6.079749
## 47.42857  8.525205 5.795482
## 47.57143  8.345803 5.521111
## 47.71429  8.172240 5.255670
## 47.85714  8.003980 4.998339
## 48.00000  7.840565 4.748417
## 48.14286  7.681599 4.505299
## 
## $長野県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 17.07753 18.87514
## 46.42857 17.34423 19.28301
## 46.57143 17.59277 19.66313
## 46.71429 17.82645 20.02050
## 46.85714 18.04763 20.35877
## 47.00000 18.25813 20.68071
## 47.14286 18.45937 20.98848
## 47.28571 18.65247 21.28380
## 47.42857 18.83834 21.56806
## 47.57143 19.01774 21.84243
## 47.71429 19.19131 22.10788
## 47.85714 19.35957 22.36521
## 48.00000 19.52298 22.61513
## 48.14286 19.68195 22.85825
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 17.47497 19.44211 15.12719 16.70603 14.93375 20.96456 17.06998 17.64820
##  [9] 17.64820 17.64820 17.64820 17.64820 17.64820 17.64820
## 
## $岐阜県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 13.64953 11.624464
## 46.42857 15.33840 13.166024
## 46.57143 10.76290  8.452594
## 46.71429 12.09589  9.655426
## 46.85714 10.09021  7.526195
## 47.00000 15.89836 13.216480
## 47.14286 11.79050  8.995714
## 47.28571 11.77278  8.662524
## 47.42857 11.47019  8.199746
## 47.57143 11.18174  7.758601
## 47.71429 10.90562  7.336310
## 47.85714 10.64037  6.930646
## 48.00000 10.38480  6.539786
## 48.14286 10.13792  6.162219
## 
## $岐阜県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 21.30041 23.32548
## 46.42857 23.54583 25.71820
## 46.57143 19.49147 21.80178
## 46.71429 21.31618 23.75664
## 46.85714 19.77730 22.34131
## 47.00000 26.03076 28.71265
## 47.14286 22.34945 25.14424
## 47.28571 23.52361 26.63387
## 47.42857 23.82620 27.09664
## 47.57143 24.11465 27.53779
## 47.71429 24.39077 27.96008
## 47.85714 24.65602 28.36574
## 48.00000 24.91159 28.75660
## 48.14286 25.15847 29.13417
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 63.32908 58.51272 54.29833 56.17464 49.96217 52.02478 59.39135 56.93308
##  [9] 55.82396 55.82396 55.82396 55.82396 55.82396 55.82396
## 
## $静岡県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 56.43843 52.79074
## 46.42857 50.62152 46.44417
## 46.57143 46.00884 41.62064
## 46.71429 47.50512 42.91576
## 46.85714 40.92861 36.14653
## 47.00000 42.64129 37.67396
## 47.14286 49.67051 44.52461
## 47.28571 46.15868 40.45505
## 47.42857 44.42698 38.39379
## 47.57143 43.95456 37.67128
## 47.71429 43.50024 36.97645
## 47.85714 43.06207 36.30634
## 48.00000 42.63846 35.65849
## 48.14286 42.22805 35.03081
## 
## $静岡県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 70.21972 73.86741
## 46.42857 66.40391 70.58126
## 46.57143 62.58783 66.97602
## 46.71429 64.84415 69.43352
## 46.85714 58.99573 63.77781
## 47.00000 61.40828 66.37560
## 47.14286 69.11219 74.25809
## 47.28571 67.70749 73.41112
## 47.42857 67.22093 73.25412
## 47.57143 67.69335 73.97663
## 47.71429 68.14767 74.67146
## 47.85714 68.58584 75.34157
## 48.00000 69.00945 75.98942
## 48.14286 69.41986 76.61710
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 196.3841 198.9305 159.6545 129.9192 139.0302 179.2215 191.8414 190.8673
##  [9] 192.3975 168.8160 150.9634 156.4335 180.5640 188.1409
## 
## $愛知県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 179.13395 170.00229
## 46.42857 178.19121 167.21250
## 46.57143 134.95135 121.87432
## 46.71429 102.10308  87.37809
## 46.85714 108.32719  92.07399
## 47.00000 145.90796 128.27282
## 47.14286 156.09903 137.17818
## 47.28571 148.20123 125.61516
## 47.42857 145.33651 120.42396
## 47.57143 117.28116  90.00026
## 47.71429  95.45568  66.07168
## 47.85714  97.17469  65.80497
## 48.00000 117.79173  84.56209
## 48.14286 122.03720  87.04403
## 
## $愛知県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 213.6342 222.7659
## 46.42857 219.6698 230.6485
## 46.57143 184.3576 197.4346
## 46.71429 157.7354 172.4604
## 46.85714 169.7333 185.9865
## 47.00000 212.5351 230.1702
## 47.14286 227.5837 246.5046
## 47.28571 233.5334 256.1195
## 47.42857 239.4584 264.3709
## 47.57143 220.3509 247.6318
## 47.71429 206.4711 235.8551
## 47.85714 215.6923 247.0620
## 48.00000 243.3363 276.5660
## 48.14286 254.2446 289.2377
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 18.30269 20.16322 18.32651 18.53665 17.49669 20.39624 21.14869 20.41816
##  [9] 20.41771 20.17361 19.56966 19.20942 20.04523 21.00151
## 
## $三重県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 14.28254 12.154401
## 46.42857 15.70401 13.343444
## 46.57143 13.67050 11.205758
## 46.71429 13.52240 10.868022
## 46.85714 12.29075  9.534890
## 47.00000 14.88532 11.968017
## 47.14286 15.45098 12.434802
## 47.28571 14.30097 11.062731
## 47.42857 14.06744 10.705814
## 47.57143 13.54898 10.042108
## 47.71429 12.72394  9.100033
## 47.85714 12.11337  8.356950
## 48.00000 12.73844  8.870462
## 48.14286 13.46328  9.472784
## 
## $三重県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 22.32283 24.45097
## 46.42857 24.62244 26.98300
## 46.57143 22.98252 25.44726
## 46.71429 23.55090 26.20528
## 46.85714 22.70264 25.45850
## 47.00000 25.90716 28.82447
## 47.14286 26.84639 29.86257
## 47.28571 26.53535 29.77359
## 47.42857 26.76797 30.12960
## 47.57143 26.79824 30.30511
## 47.71429 26.41538 30.03929
## 47.85714 26.30546 30.06188
## 48.00000 27.35202 31.22000
## 48.14286 28.53974 32.53023
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 7.737380 6.302532 7.451005 7.043494 7.398280 7.284142 7.394400 7.363040
##  [9] 7.397534 7.389153 7.400025 7.397877 7.401331 7.400818
## 
## $滋賀県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%           95%
## 46.28571 3.839063  1.7754187831
## 46.42857 2.330104  0.2272269110
## 46.57143 3.060389  0.7361367132
## 46.71429 2.561292  0.1885575213
## 46.85714 2.755358  0.2975438477
## 47.00000 2.552905  0.0483387763
## 47.14286 2.559704  0.0003714787
## 47.28571 2.445185 -0.1581698634
## 47.42857 2.393773 -0.2550585151
## 47.57143 2.306243 -0.3844865483
## 47.71429 2.238176 -0.4943422318
## 47.85714 2.159891 -0.6129314963
## 48.00000 2.088069 -0.7246028029
## 48.14286 2.013831 -0.8378676797
## 
## $滋賀県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 11.63570 13.69934
## 46.42857 10.27496 12.37784
## 46.57143 11.84162 14.16587
## 46.71429 11.52570 13.89843
## 46.85714 12.04120 14.49902
## 47.00000 12.01538 14.51995
## 47.14286 12.22909 14.78843
## 47.28571 12.28089 14.88425
## 47.42857 12.40130 15.05013
## 47.57143 12.47206 15.16279
## 47.71429 12.56187 15.29439
## 47.85714 12.63586 15.40869
## 48.00000 12.71459 15.52727
## 48.14286 12.78781 15.63950
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 28.81213 27.56309 25.72169 23.24917 28.80196 29.78574 26.12464 26.63166
##  [9] 28.19276 26.23824 24.88366 23.83873 27.11221 28.12364
## 
## $京都府$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 21.82766 18.130314
## 46.42857 19.92148 15.876261
## 46.57143 17.93715 13.816270
## 46.71429 15.32429 11.129110
## 46.85714 20.73917 16.470989
## 47.00000 21.58736 17.247402
## 47.14286 17.79289 13.382321
## 47.28571 17.93911 13.337554
## 47.42857 19.28942 14.576284
## 47.57143 17.17480 12.376904
## 47.71429 15.66288 10.781701
## 47.85714 14.46327  9.500198
## 48.00000 17.58457 12.540939
## 48.14286 18.44621 13.323290
## 
## $京都府$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 35.79659 39.49394
## 46.42857 35.20470 39.24992
## 46.57143 33.50622 37.62710
## 46.71429 31.17405 35.36923
## 46.85714 36.86475 41.13293
## 47.00000 37.98411 42.32407
## 47.14286 34.45640 38.86697
## 47.28571 35.32421 39.92577
## 47.42857 37.09609 41.80923
## 47.57143 35.30168 40.09958
## 47.71429 34.10443 38.98561
## 47.85714 33.21419 38.17726
## 48.00000 36.63985 41.68348
## 48.14286 37.80107 42.92399
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 368.5208 405.8419 436.7123 289.5870 305.5122 360.0276 380.0243 406.3009
##  [9] 434.8379 472.7551 351.1949 329.6723 384.3169 396.1539
## 
## $大阪府$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 336.0277 318.8268
## 46.42857 368.5860 348.8639
## 46.57143 397.8157 377.2251
## 46.71429 249.1162 227.6922
## 46.85714 263.5261 241.3001
## 47.00000 316.5791 293.5789
## 47.14286 335.1611 311.4120
## 47.28571 354.3588 326.8623
## 47.42857 379.3238 349.9364
## 47.57143 414.8868 384.2532
## 47.71429 291.0646 259.2335
## 47.85714 267.3620 234.3769
## 48.00000 319.9003 285.8002
## 48.14286 329.6978 294.5181
## 
## $大阪府$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 401.0140 418.2149
## 46.42857 443.0978 462.8199
## 46.57143 475.6089 496.1995
## 46.71429 330.0578 351.4817
## 46.85714 347.4982 369.7243
## 47.00000 403.4760 426.4762
## 47.14286 424.8875 448.6367
## 47.28571 458.2430 485.7395
## 47.42857 490.3519 519.7393
## 47.57143 530.6233 561.2569
## 47.71429 411.3252 443.1562
## 47.85714 391.9826 424.9677
## 48.00000 448.7334 482.8335
## 48.14286 462.6100 497.7898
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 173.1913 167.3934 153.5046 129.9999 119.6827 130.7516 159.7150 156.4521
##  [9] 153.2135 150.7577 149.2940 148.6954 148.6901 148.9953
## 
## $兵庫県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 160.9754 154.50877
## 46.42857 153.5953 146.29104
## 46.57143 139.3173 131.80693
## 46.71429 115.6231 108.01256
## 46.85714 105.0550  97.31148
## 47.00000 115.6669 107.68148
## 47.14286 143.8981 135.52519
## 47.28571 137.5953 127.61312
## 47.42857 132.4856 121.51298
## 47.57143 128.6551 116.95468
## 47.71429 126.0038 113.67479
## 47.85714 124.2571 111.32023
## 48.00000 123.0814 109.52498
## 48.14286 122.1829 107.98927
## 
## $兵庫県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 185.4071 191.8737
## 46.42857 181.1915 188.4957
## 46.57143 167.6920 175.2023
## 46.71429 144.3766 151.9872
## 46.85714 134.3105 142.0540
## 47.00000 145.8363 153.8217
## 47.14286 175.5318 183.9048
## 47.28571 175.3089 185.2911
## 47.42857 173.9414 184.9140
## 47.57143 172.8604 184.5608
## 47.71429 172.5841 184.9132
## 47.85714 173.1337 186.0706
## 48.00000 174.2988 187.8552
## 48.14286 175.8077 190.0014
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 17.90261 17.78477 16.77748 17.83103 17.13618 16.39190 17.65940 17.30735
##  [9] 17.30735 17.30735 17.30735 17.30735 17.30735 17.30735
## 
## $奈良県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 13.56168 11.263738
## 46.42857 13.26280 10.869023
## 46.57143 12.08145  9.595530
## 46.71429 12.96717 10.392394
## 46.85714 12.11008  9.449429
## 47.00000 11.20865  8.464796
## 47.14286 12.32361  9.499011
## 47.28571 11.67905  8.699612
## 47.42857 11.50277  8.430018
## 47.57143 11.33169  8.168373
## 47.71429 11.16538  7.914014
## 47.85714 11.00345  7.666363
## 48.00000 10.84557  7.424916
## 48.14286 10.69147  7.189229
## 
## $奈良県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 22.24353 24.54148
## 46.42857 22.30673 24.70051
## 46.57143 21.47350 23.95943
## 46.71429 22.69489 25.26966
## 46.85714 22.16228 24.82293
## 47.00000 21.57516 24.31901
## 47.14286 22.99519 25.81979
## 47.28571 22.93565 25.91509
## 47.42857 23.11193 26.18468
## 47.57143 23.28301 26.44633
## 47.71429 23.44932 26.70069
## 47.85714 23.61125 26.94834
## 48.00000 23.76913 27.18978
## 48.14286 23.92323 27.42547
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 6.190380 7.396992 7.086326 6.797096 6.794640 7.562387 6.920089 7.283828
##  [9] 6.915812 7.230250 6.961589 7.191137 6.995008 7.162584
## 
## $和歌山県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 4.128862 3.037560
## 46.42857 5.142198 3.948583
## 46.57143 4.758963 3.526931
## 46.71429 4.314413 3.000161
## 46.85714 4.236067 2.881640
## 47.00000 4.873000 3.449324
## 47.14286 4.153475 2.688918
## 47.28571 4.472054 2.983590
## 47.42857 4.052976 2.537482
## 47.57143 4.283243 2.723191
## 47.71429 3.961524 2.373385
## 47.85714 4.115132 2.486793
## 48.00000 3.865014 2.208095
## 48.14286 3.962798 2.268934
## 
## $和歌山県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571  8.251898  9.343199
## 46.42857  9.651785 10.845401
## 46.57143  9.413689 10.645721
## 46.71429  9.279778 10.594031
## 46.85714  9.353213 10.707640
## 47.00000 10.251775 11.675451
## 47.14286  9.686703 11.151259
## 47.28571 10.095603 11.584066
## 47.42857  9.778648 11.294142
## 47.57143 10.177258 11.737309
## 47.71429  9.961655 11.549794
## 47.85714 10.267142 11.895481
## 48.00000 10.125003 11.781922
## 48.14286 10.362369 12.056233
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 0.9307719 0.9105079 0.7798891 0.6102268 0.5666744 0.5219019 0.4866000
##  [8] 0.4699979 0.4571306 0.4483587 0.4432261 0.4396384 0.4372807 0.4357930
## 
## $鳥取県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                  80%        95%
## 46.28571 -0.01074425 -0.5091528
## 46.42857 -0.04364696 -0.5487460
## 46.57143 -0.18606011 -0.6974028
## 46.71429 -0.37349821 -0.8942508
## 46.85714 -0.42047373 -0.9430384
## 47.00000 -0.46775158 -0.9916425
## 47.14286 -0.50494066 -1.0298306
## 47.28571 -0.52251800 -1.0479242
## 47.42857 -0.53614017 -1.0619460
## 47.57143 -0.54551485 -1.0716397
## 47.71429 -0.55113077 -1.0775115
## 47.85714 -0.55514832 -1.0817566
## 48.00000 -0.55789962 -1.0847162
## 48.14286 -0.55975590 -1.0867676
## 
## $鳥取県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 1.872288 2.370697
## 46.42857 1.864663 2.369762
## 46.57143 1.745838 2.257181
## 46.71429 1.593952 2.114704
## 46.85714 1.553823 2.076387
## 47.00000 1.511555 2.035446
## 47.14286 1.478141 2.003031
## 47.28571 1.462514 1.987920
## 47.42857 1.450401 1.976207
## 47.57143 1.442232 1.968357
## 47.71429 1.437583 1.963964
## 47.85714 1.434425 1.961033
## 48.00000 1.432461 1.959278
## 48.14286 1.431342 1.958354
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 0.4542587 0.4542587 0.4542587 0.4542587 0.4542587 0.4542587 0.4542587
##  [8] 0.4542587 0.4542587 0.4542587 0.4542587 0.4542587 0.4542587 0.4542587
## 
## $島根県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%      95%
## 46.28571 -6.198861 -9.72081
## 46.42857 -6.198861 -9.72081
## 46.57143 -6.198861 -9.72081
## 46.71429 -6.198861 -9.72081
## 46.85714 -6.198861 -9.72081
## 47.00000 -6.198861 -9.72081
## 47.14286 -6.198861 -9.72081
## 47.28571 -6.198861 -9.72081
## 47.42857 -6.198861 -9.72081
## 47.57143 -6.198861 -9.72081
## 47.71429 -6.198861 -9.72081
## 47.85714 -6.198861 -9.72081
## 48.00000 -6.198861 -9.72081
## 48.14286 -6.198861 -9.72081
## 
## $島根県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 7.107378 10.62933
## 46.42857 7.107378 10.62933
## 46.57143 7.107378 10.62933
## 46.71429 7.107378 10.62933
## 46.85714 7.107378 10.62933
## 47.00000 7.107378 10.62933
## 47.14286 7.107378 10.62933
## 47.28571 7.107378 10.62933
## 47.42857 7.107378 10.62933
## 47.57143 7.107378 10.62933
## 47.71429 7.107378 10.62933
## 47.85714 7.107378 10.62933
## 48.00000 7.107378 10.62933
## 48.14286 7.107378 10.62933
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 12.44575 15.77654 12.19248 12.21469 11.24137 12.27825 12.53696 12.35176
##  [9] 12.34708 12.34415 12.34231 12.34115 12.34042 12.33997
## 
## $岡山県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571  9.256566  7.568312
## 46.42857 12.374854 10.574107
## 46.57143  8.659035  6.788540
## 46.71429  8.588887  6.669504
## 46.85714  7.544080  5.586853
## 47.00000  8.521221  6.532368
## 47.14286  8.727129  6.710326
## 47.28571  8.307088  6.165966
## 47.42857  8.209116  6.018608
## 47.57143  8.127335  5.895090
## 47.71429  8.055289  5.785880
## 47.85714  7.989330  5.685616
## 48.00000  7.927326  5.591173
## 48.14286  7.867997  5.500678
## 
## $岡山県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 15.63494 17.32319
## 46.42857 19.17824 20.97898
## 46.57143 15.72593 17.59642
## 46.71429 15.84049 17.75987
## 46.85714 14.93866 16.89589
## 47.00000 16.03529 18.02414
## 47.14286 16.34679 18.36360
## 47.28571 16.39644 18.53756
## 47.42857 16.48505 18.67556
## 47.57143 16.56096 18.79321
## 47.71429 16.62932 18.89873
## 47.85714 16.69297 18.99668
## 48.00000 16.75352 19.08968
## 48.14286 16.81194 19.17926
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1]  9.894550  6.321861  9.363531 10.485882  8.387816 10.088855  8.714067
##  [8]  9.583380  9.445738  9.412437 10.035651  9.501795  8.565678 10.201270
## 
## $広島県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%         95%
## 46.28571 6.266704  4.34623913
## 46.42857 2.365327  0.27086395
## 46.57143 5.231878  3.04471276
## 46.71429 5.695990  3.16037434
## 46.85714 3.028998  0.19221066
## 47.00000 4.463761  1.48601679
## 47.14286 2.790506 -0.34523828
## 47.28571 3.379127  0.09479415
## 47.42857 2.956153 -0.47922631
## 47.57143 2.686292 -0.87431465
## 47.71429 3.082647 -0.59805015
## 47.85714 2.309668 -1.49761379
## 48.00000 1.144942 -2.78335789
## 48.14286 2.568600 -1.47189214
## 
## $広島県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 13.52239 15.44286
## 46.42857 10.27840 12.37286
## 46.57143 13.49518 15.68235
## 46.71429 15.27577 17.81139
## 46.85714 13.74663 16.58342
## 47.00000 15.71395 18.69169
## 47.14286 14.63763 17.77337
## 47.28571 15.78763 19.07197
## 47.42857 15.93532 19.37070
## 47.57143 16.13858 19.69919
## 47.71429 16.98865 20.66935
## 47.85714 16.69392 20.50120
## 48.00000 15.98641 19.91471
## 48.14286 17.83394 21.87443
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 10.410160  5.829853  6.287012  1.950969  4.158182  3.184216  5.808136
##  [8]  4.818219  5.634270  3.061490  4.090249  1.717723  4.332086  2.112880
## 
## $山口県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                  80%        95%
## 46.28571  7.81224645  6.4369942
## 46.42857  3.00418532  1.5083671
## 46.57143  3.24146991  1.6292571
## 46.71429 -1.17723123 -2.8332005
## 46.85714  0.92658704 -0.7841160
## 47.00000 -0.08069203 -1.8090301
## 47.14286  2.48789789  0.7302702
## 47.28571  1.34795978 -0.4890846
## 47.42857  2.07952946  0.1977634
## 47.57143 -0.53089922 -2.4325950
## 47.71429  0.45165784 -1.4744960
## 47.85714 -1.93978000 -3.8759453
## 48.00000  0.64634104 -1.3047745
## 48.14286 -1.58344078 -3.5401551
## 
## $山口県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 13.008073 14.383325
## 46.42857  8.655521 10.151339
## 46.57143  9.332555 10.944768
## 46.71429  5.079170  6.735139
## 46.85714  7.389777  9.100480
## 47.00000  6.449125  8.177463
## 47.14286  9.128373 10.886001
## 47.28571  8.288479 10.125523
## 47.42857  9.189011 11.070777
## 47.57143  6.653879  8.555574
## 47.71429  7.728840  9.654994
## 47.85714  5.375227  7.311392
## 48.00000  8.017831  9.968947
## 48.14286  5.809202  7.765916
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 1.0574208 0.7389664 0.8022547 0.6619973 0.3428509 0.4153643 0.6125265
##  [8] 0.5156596 0.4894045 0.5832880 0.7025193 0.7380658 0.7569692 0.6135750
## 
## $徳島県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                 80%       95%
## 46.28571 -0.6490818 -1.552450
## 46.42857 -1.0506147 -1.997962
## 46.57143 -1.0056008 -1.962622
## 46.71429 -1.1639497 -2.130548
## 46.85714 -1.5010102 -2.477091
## 47.00000 -1.4462385 -2.431711
## 47.14286 -1.2666505 -2.261427
## 47.28571 -1.4553247 -2.498701
## 47.42857 -1.5147622 -2.575704
## 47.57143 -1.4442867 -2.517620
## 47.71429 -1.3481962 -2.433779
## 47.85714 -1.3355322 -2.433229
## 48.00000 -1.3392616 -2.448939
## 48.14286 -1.5050469 -2.626578
## 
## $徳島県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 2.763923 3.667291
## 46.42857 2.528547 3.475894
## 46.57143 2.610110 3.567131
## 46.71429 2.487944 3.454542
## 46.85714 2.186712 3.162793
## 47.00000 2.276967 3.262440
## 47.14286 2.491703 3.486480
## 47.28571 2.486644 3.530020
## 47.42857 2.493571 3.554513
## 47.57143 2.610863 3.684196
## 47.71429 2.753235 3.838818
## 47.85714 2.811664 3.909360
## 48.00000 2.853200 3.962878
## 48.14286 2.732197 3.853727
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 1.207928 1.302040 1.302040 1.302040 1.302040 1.302040 1.302040 1.302040
##  [9] 1.302040 1.302040 1.302040 1.302040 1.302040 1.302040
## 
## $香川県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                 80%        95%
## 46.28571 -0.2298885 -0.9910224
## 46.42857 -0.1910822 -0.9814932
## 46.57143 -0.1984146 -0.9927071
## 46.71429 -0.2057113 -1.0038665
## 46.85714 -0.2129729 -1.0149722
## 47.00000 -0.2201998 -1.0260248
## 47.14286 -0.2273926 -1.0370253
## 47.28571 -0.2345518 -1.0479742
## 47.42857 -0.2416777 -1.0588724
## 47.57143 -0.2487709 -1.0697205
## 47.71429 -0.2558318 -1.0805192
## 47.85714 -0.2628608 -1.0912692
## 48.00000 -0.2698584 -1.1019711
## 48.14286 -0.2768250 -1.1126256
## 
## $香川県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 2.645744 3.406878
## 46.42857 2.795162 3.585573
## 46.57143 2.802494 3.596787
## 46.71429 2.809791 3.607946
## 46.85714 2.817053 3.619052
## 47.00000 2.824280 3.630105
## 47.14286 2.831472 3.641105
## 47.28571 2.838631 3.652054
## 47.42857 2.845757 3.662952
## 47.57143 2.852851 3.673800
## 47.71429 2.859911 3.684599
## 47.85714 2.866941 3.695349
## 48.00000 2.873938 3.706051
## 48.14286 2.880905 3.716705
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 18.29625 17.76561 17.86258 17.78005 17.85029 17.79051 17.84138 17.79809
##  [9] 17.83494 17.80358 17.83027 17.80755 17.82688 17.81043
## 
## $愛媛県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 16.07860 14.90464
## 46.42857 15.18234 13.81484
## 46.57143 14.95393 13.41418
## 46.71429 14.68880 13.05240
## 46.85714 14.49930 12.72539
## 47.00000 14.26872 12.40440
## 47.14286 14.10021 12.11976
## 47.28571 13.89570 11.82989
## 47.42857 13.74039 11.57287
## 47.57143 13.55613 11.30767
## 47.71429 13.41020 11.07035
## 47.85714 13.24200 10.82514
## 48.00000 13.10337 10.60290
## 48.14286 12.94812 10.37417
## 
## $愛媛県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 20.51390 21.68786
## 46.42857 20.34888 21.71638
## 46.57143 20.77123 22.31098
## 46.71429 20.87130 22.50771
## 46.85714 21.20128 22.97518
## 47.00000 21.31231 23.17663
## 47.14286 21.58256 23.56301
## 47.28571 21.70049 23.76629
## 47.42857 21.92948 24.09700
## 47.57143 22.05103 24.29949
## 47.71429 22.25034 24.59018
## 47.85714 22.37311 24.78997
## 48.00000 22.55039 25.05087
## 48.14286 22.67274 25.24669
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 0.4960377 0.4931621 0.4905078 0.4880579 0.4857966 0.4837093 0.4817827
##  [8] 0.4800045 0.4783631 0.4768481 0.4754497 0.4741590 0.4729676 0.4718679
## 
## $高知県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%       95%
## 46.28571 -1.180340 -2.067761
## 46.42857 -1.204415 -2.103058
## 46.57143 -1.224924 -2.133019
## 46.71429 -1.242440 -2.158510
## 46.85714 -1.257435 -2.180245
## 47.00000 -1.270297 -2.198812
## 47.14286 -1.281352 -2.214699
## 47.28571 -1.290870 -2.228314
## 47.42857 -1.299078 -2.239999
## 47.57143 -1.306169 -2.250042
## 47.71429 -1.312305 -2.258685
## 47.85714 -1.317621 -2.266132
## 48.00000 -1.322235 -2.272558
## 48.14286 -1.326246 -2.278110
## 
## $高知県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 2.172416 3.059836
## 46.42857 2.190739 3.089383
## 46.57143 2.205940 3.114035
## 46.71429 2.218556 3.134626
## 46.85714 2.229028 3.151838
## 47.00000 2.237716 3.166230
## 47.14286 2.244917 3.178264
## 47.28571 2.250879 3.188323
## 47.42857 2.255804 3.196725
## 47.57143 2.259866 3.203738
## 47.71429 2.263204 3.209584
## 47.85714 2.265939 3.214450
## 48.00000 2.268170 3.218493
## 48.14286 2.269982 3.221845
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 47.15620 42.52092 39.80973 32.21459 33.89145 40.65963 48.36252 48.24334
##  [9] 46.32461 44.87226 39.76660 40.96898 45.65564 50.97764
## 
## $福岡県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 33.25281 25.892793
## 46.42857 26.05814 17.343273
## 46.57143 21.86088 12.359332
## 46.71429 13.08855  2.963831
## 46.85714 13.71407  3.032814
## 47.00000 19.50068  8.299811
## 47.14286 26.27105 14.576532
## 47.28571 23.65134 10.633119
## 47.42857 20.07845  6.184563
## 47.57143 17.24192  2.615295
## 47.71429 10.86989 -4.427101
## 47.85714 10.87517 -5.055537
## 48.00000 14.41558 -2.121909
## 48.14286 18.63351  1.511565
## 
## $福岡県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 61.05960  68.41961
## 46.42857 58.98370  67.69857
## 46.57143 57.75859  67.26014
## 46.71429 51.34064  61.46535
## 46.85714 54.06883  64.75009
## 47.00000 61.81858  73.01945
## 47.14286 70.45399  82.14851
## 47.28571 72.83534  85.85356
## 47.42857 72.57078  86.46466
## 47.57143 72.50261  87.12923
## 47.71429 68.66330  83.96029
## 47.85714 71.06280  86.99350
## 48.00000 76.89571  93.43320
## 48.14286 83.32176 100.44371
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 1.732079 1.732079 1.732079 1.732079 1.732079 1.732079 1.732079 1.732079
##  [9] 1.732079 1.732079 1.732079 1.732079 1.732079 1.732079
## 
## $佐賀県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                 80%       95%
## 46.28571 -0.2980572 -1.372746
## 46.42857 -0.3690617 -1.481338
## 46.57143 -0.4377440 -1.586379
## 46.71429 -0.5043179 -1.688195
## 46.85714 -0.5689666 -1.787066
## 47.00000 -0.6318478 -1.883235
## 47.14286 -0.6930992 -1.976911
## 47.28571 -0.7528413 -2.068279
## 47.42857 -0.8111804 -2.157501
## 47.57143 -0.8682109 -2.244721
## 47.71429 -0.9240172 -2.330070
## 47.85714 -0.9786749 -2.413661
## 48.00000 -1.0322520 -2.495600
## 48.14286 -1.0848103 -2.575981
## 
## $佐賀県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 3.762215 4.836904
## 46.42857 3.833220 4.945496
## 46.57143 3.901902 5.050537
## 46.71429 3.968476 5.152353
## 46.85714 4.033125 5.251224
## 47.00000 4.096006 5.347393
## 47.14286 4.157257 5.441069
## 47.28571 4.216999 5.532437
## 47.42857 4.275338 5.621659
## 47.57143 4.332369 5.708879
## 47.71429 4.388175 5.794228
## 47.85714 4.442833 5.877819
## 48.00000 4.496410 5.959758
## 48.14286 4.548968 6.040139
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 1.370457 1.410590 1.119137 1.028866 1.120537 1.121283 1.117119 1.322929
##  [9] 1.389627 1.101652 1.126478 1.101904 1.102038 1.107396
## 
## $長崎県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                 80%       95%
## 46.28571 -0.7335343 -1.847320
## 46.42857 -0.9320153 -2.172116
## 46.57143 -1.7055027 -3.200776
## 46.71429 -2.0799112 -3.725598
## 46.85714 -2.3060752 -4.120014
## 47.00000 -2.5684379 -4.521658
## 47.14286 -2.8312504 -4.921391
## 47.28571 -3.0096594 -5.303193
## 47.42857 -3.2257440 -5.668974
## 47.57143 -3.8131372 -6.414869
## 47.71429 -4.0543779 -6.796957
## 47.85714 -4.3398043 -7.220471
## 48.00000 -4.5847586 -7.595167
## 48.14286 -4.8162234 -7.951998
## 
## $長崎県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 3.474448  4.588234
## 46.42857 3.753196  4.993297
## 46.57143 3.943776  5.439050
## 46.71429 4.137644  5.783331
## 46.85714 4.547150  6.361089
## 47.00000 4.811004  6.764224
## 47.14286 5.065488  7.155628
## 47.28571 5.655518  7.949052
## 47.42857 6.004999  8.448229
## 47.57143 6.016440  8.618172
## 47.71429 6.307334  9.049913
## 47.85714 6.543612  9.424278
## 48.00000 6.788835  9.799243
## 48.14286 7.031015 10.166789
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 10.008934  6.920074  8.096867  7.936089  7.789225  7.655071  7.532528
##  [8]  7.420590  7.318339  7.224938  7.139620  7.061686  6.990496  6.925468
## 
## $熊本県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                  80%        95%
## 46.28571  5.68599620  3.3975713
## 46.42857  1.34504468 -1.6061975
## 46.57143  2.42553876 -0.5766812
## 46.71429  1.98547151 -1.1645951
## 46.85714  1.60568910 -1.6676771
## 47.00000  1.27506665 -2.1023038
## 47.14286  0.98523656 -2.4806901
## 47.28571  0.72970535 -2.8122349
## 47.42857  0.50331345 -3.1043432
## 47.57143  0.30188529 -3.3629573
## 47.71429  0.12199182 -3.5929160
## 47.85714 -0.03921657 -3.7982071
## 48.00000 -0.18413143 -3.9821498
## 48.14286 -0.31477654 -4.1475303
## 
## $熊本県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 14.33187 16.62030
## 46.42857 12.49510 15.44635
## 46.57143 13.76820 16.77042
## 46.71429 13.88671 17.03677
## 46.85714 13.97276 17.24613
## 47.00000 14.03508 17.41245
## 47.14286 14.07982 17.54575
## 47.28571 14.11147 17.65341
## 47.42857 14.13336 17.74102
## 47.57143 14.14799 17.81283
## 47.71429 14.15725 17.87216
## 47.85714 14.16259 17.92158
## 48.00000 14.16512 17.96314
## 48.14286 14.16571 17.99847
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 9.924511 8.833460 7.570045 7.821604 7.041926 7.549632 7.852794 7.946888
##  [9] 8.229135 8.054059 7.980697 7.885447 7.785793 7.831784
## 
## $大分県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 8.185041 7.264221
## 46.42857 6.889939 5.861101
## 46.57143 5.320118 4.129079
## 46.71429 5.425898 4.157688
## 46.85714 4.623344 3.343023
## 47.00000 5.073723 3.763057
## 47.14286 5.346535 4.019801
## 47.28571 5.375988 4.015036
## 47.42857 5.562149 4.150333
## 47.57143 5.298260 3.839428
## 47.71429 5.124917 3.613158
## 47.85714 4.951738 3.398727
## 48.00000 4.784909 3.196337
## 48.14286 4.768421 3.146775
## 
## $大分県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                80%      95%
## 46.28571 11.663981 12.58480
## 46.42857 10.776981 11.80582
## 46.57143  9.819972 11.01101
## 46.71429 10.217310 11.48552
## 46.85714  9.460509 10.74083
## 47.00000 10.025540 11.33621
## 47.14286 10.359053 11.68579
## 47.28571 10.517788 11.87874
## 47.42857 10.896120 12.30794
## 47.57143 10.809858 12.26869
## 47.71429 10.836476 12.34823
## 47.85714 10.819155 12.37217
## 48.00000 10.786677 12.37525
## 48.14286 10.895146 12.51679
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 8.485456 8.749405 8.749405 8.749405 8.749405 8.749405 8.749405 8.749405
##  [9] 8.749405 8.749405 8.749405 8.749405 8.749405 8.749405
## 
## $宮崎県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%         95%
## 46.28571 5.585267  4.04999981
## 46.42857 5.690348  4.07098140
## 46.57143 5.333739  3.52559474
## 46.71429 5.010994  3.03199826
## 46.85714 4.713979  2.57775318
## 47.00000 4.437374  2.15472287
## 47.14286 4.177474  1.75723948
## 47.28571 3.931573  1.38116743
## 47.42857 3.697628  1.02337942
## 47.57143 3.474048  0.68144258
## 47.71429 3.259566  0.35342002
## 47.85714 3.053153  0.03773982
## 48.00000 2.853964 -0.26689449
## 48.14286 2.661288 -0.56156717
## 
## $宮崎県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 11.38564 12.92091
## 46.42857 11.80846 13.42783
## 46.57143 12.16507 13.97322
## 46.71429 12.48782 14.46681
## 46.85714 12.78483 14.92106
## 47.00000 13.06144 15.34409
## 47.14286 13.32134 15.74157
## 47.28571 13.56724 16.11764
## 47.42857 13.80118 16.47543
## 47.57143 14.02476 16.81737
## 47.71429 14.23924 17.14539
## 47.85714 14.44566 17.46107
## 48.00000 14.64485 17.76570
## 48.14286 14.83752 18.06038
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 4.434458 4.710744 4.255853 4.243956 4.243956 4.243956 4.243956 4.243956
##  [9] 4.243956 4.243956 4.243956 4.243956 4.243956 4.243956
## 
## $鹿児島県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##                  80%       95%
## 46.28571  0.35518909 -1.804245
## 46.42857 -0.09314916 -2.636177
## 46.57143 -0.76650098 -3.425175
## 46.71429 -0.87601711 -3.586367
## 46.85714 -0.89367580 -3.613374
## 47.00000 -0.91127400 -3.640288
## 47.14286 -0.92881233 -3.667111
## 47.28571 -0.94629140 -3.693843
## 47.42857 -0.96371181 -3.720485
## 47.57143 -0.98107413 -3.747038
## 47.71429 -0.99837895 -3.773504
## 47.85714 -1.01562684 -3.799882
## 48.00000 -1.03281835 -3.826174
## 48.14286 -1.04995403 -3.852381
## 
## $鹿児島県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 8.513726 10.67316
## 46.42857 9.514638 12.05767
## 46.57143 9.278208 11.93688
## 46.71429 9.363930 12.07428
## 46.85714 9.381589 12.10129
## 47.00000 9.399187 12.12820
## 47.14286 9.416725 12.15502
## 47.28571 9.434204 12.18176
## 47.42857 9.451625 12.20840
## 47.57143 9.468987 12.23495
## 47.71429 9.486292 12.26142
## 47.85714 9.503540 12.28779
## 48.00000 9.520731 12.31409
## 48.14286 9.537867 12.34029
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##  [1] 42.98121 45.45282 41.15559 38.04361 38.37459 40.36099 51.96756 45.05241
##  [9] 45.05241 45.05241 45.05241 45.05241 45.05241 45.05241
## 
## $沖縄県$lower
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%       95%
## 46.28571 30.62262 24.080369
## 46.42857 32.06907 24.984142
## 46.57143 26.81981 19.230914
## 46.71429 22.81521 14.753780
## 46.85714 22.30306 13.795310
## 47.00000 23.48841 14.556613
## 47.14286 34.33029 24.993676
## 47.28571 25.46259 15.092361
## 47.42857 24.39795 13.464144
## 47.57143 23.38557 11.915835
## 47.71429 22.41842 10.436711
## 47.85714 21.49094  9.018251
## 48.00000 20.59861  7.653551
## 48.14286 19.73772  6.336926
## 
## $沖縄県$upper
## Time Series:
## Start = c(46, 3) 
## End = c(48, 2) 
## Frequency = 7 
##               80%      95%
## 46.28571 55.33981 61.88206
## 46.42857 58.83656 65.92149
## 46.57143 55.49136 63.08026
## 46.71429 53.27201 61.33344
## 46.85714 54.44611 62.95387
## 47.00000 57.23356 66.16536
## 47.14286 69.60484 78.94145
## 47.28571 64.64224 75.01247
## 47.42857 65.70687 76.64068
## 47.57143 66.71926 78.18899
## 47.71429 67.68640 79.66812
## 47.85714 68.61389 81.08658
## 48.00000 69.50621 82.45128
## 48.14286 70.36711 83.76790